HONAM uses all feature combinations to model interactions.
However, modeling all interactions slows down the execution time and can reduce the predictive performance because some interactions may be noisy [1]}.
If HONAM can adaptively learn feature interactions, it may have higher predictive performance, interpretability, and faster execution time.
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